While the futuristic notion of a world filled with robotic workers, companions and even enemies is worlds away, pragmatic Artificial Intelligence knows many different definitions, but in general it can be defined as a machine completing complex tasks intelligently, meaning that it mirrors human intelligence and evolves with time. () is moving full steam ahead with no plans for stopping. In all of its forms and variations, it’s already starting to deliver real value to businesses.
Insurers are realizing the benefits can bring, automating manual underwriting processes, providing faster and better customer service and predicting risk. Retailers are seeing how can help them better understand shopper preferences and consumer behavior. What’s more, health systems are able to predict the likelihood of patient readmissions or how to augment diagnoses and treatment plans.
Major accelerators of include the sheer computing power that is available today in the form of graphics processing units (GPUs), the growth of the data scientist profession and increasingly sophisticated An algorithm is a fixed set of instructions for a computer. It can be very simple like "as long as the incoming number is smaller than 10, print "Hello World!". It can also be very complicated such as the algorithms behind self-driving cars..
Perhaps the biggest factor feeding the swell of today is the proliferation of data being produced, stored and shared from Internet of Things (IoT)-based sensors, web pages, databases, social media sites and other tools. It’s interesting that most companies never made a conscious decision to accumulate data, per se — it just sort of happened over time. Yet , which unlocks the correlations and mysteries of all that data, requires a much more deliberate pathway to adoption and is often fraught with skepticism.
Kicking The Tires
This skepticism may account for the fact that in a McKinsey survey of more than 3,000 companies, worldwide adoption outside the tech sector is still at an early, experimental stage. It found that the early adopters tend to be the larger firms deploying across their technology groups. They’re adopting to increase revenue as well as reduce costs. It also found that among those firms, it has the full support of executive leadership.
Companies that have not adopted technology at scale or in a core part of their business are not yet sure of the business case for or of the returns they can expect from it. The adoption pattern is showing a widening gap between early adopters and laggards. Why are companies so reluctant to join the bandwagon?
Companies that have not adopted technology are unsure of the business case for or of the returns they can expect if they make the costly investment. They don’t understand what specific business problems they may be able to solve using .
Wait And See Attitude
In reality, , Chatbots are computer programs which were engineered to converse in spoken or written form with humans. They are usually used in dialogue systems with a limited topic range. For example, they can answer basic customer questions or help you buy the correct train ticket. and machine applications have only taken off in the last few years, led by the large technology companies. Many mid-size firms are waiting to hear about measurable return on investment (ROI) from the early adopters, and for many of them, those metrics may be premature.
Fear Of The Unknown
Companies unfamiliar with may envision it as something to be feared, worrying that it will cause harm instead of good. They also fear that it will take away human jobs.
Probably one of the biggest reasons companies don’t adopt is the sheer cost of it. While partnering with a service provider can substantially offset the costs of deploying , companies thinking about initiating an project in-house would have to invest in costly GPU capacity, experienced data scientists and of course a complete disruption to their core business processes. Without complete faith in , that’s a tough risk to take. […]